PACT-3D, a deep learning algorithm for pneumoperitoneum detection in abdominal CT scans
Abstract Delays or misdiagnoses in detecting pneumoperitoneum can significantly increase mortality and morbidity. We developed and validated a deep learning model designed to identify pneumoperitoneum in computed tomography images. The model is trained on abdominal scans from Far Eastern Memorial Ho...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-11-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-54043-1 |
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| author | I-Min Chiu Teng-Yi Huang David Ouyang Wei-Che Lin Yi-Ju Pan Chia-Yin Lu Kuei-Hong Kuo |
| author_facet | I-Min Chiu Teng-Yi Huang David Ouyang Wei-Che Lin Yi-Ju Pan Chia-Yin Lu Kuei-Hong Kuo |
| author_sort | I-Min Chiu |
| collection | DOAJ |
| description | Abstract Delays or misdiagnoses in detecting pneumoperitoneum can significantly increase mortality and morbidity. We developed and validated a deep learning model designed to identify pneumoperitoneum in computed tomography images. The model is trained on abdominal scans from Far Eastern Memorial Hospital (January 2012–December 2021) and evaluated using a simulated test set (14,039 scans) and a prospective test set (6351 scans) collected from the same center between December 2022 and May 2023. External validation included 480 scans from Cedars-Sinai Medical Center. Overall, the model achieves a sensitivity of 0.81–0.83 and a specificity of 0.97–0.99 across retrospective, prospective, and external validation; sensitivity improves to 0.92–0.98 when cases with a small amount of free air (total volume <10 ml) are excluded. These findings suggest that the model can deliver accurate and consistent predictions for pneumoperitoneum in computed tomography scans with segmented masks, potentially accelerating diagnostic and treatment workflows in emergency care. |
| format | Article |
| id | doaj-art-af2ca42896254049aeeb4cd1e9902be3 |
| institution | OA Journals |
| issn | 2041-1723 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-af2ca42896254049aeeb4cd1e9902be32025-08-20T02:13:26ZengNature PortfolioNature Communications2041-17232024-11-011511710.1038/s41467-024-54043-1PACT-3D, a deep learning algorithm for pneumoperitoneum detection in abdominal CT scansI-Min Chiu0Teng-Yi Huang1David Ouyang2Wei-Che Lin3Yi-Ju Pan4Chia-Yin Lu5Kuei-Hong Kuo6Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical CenterDepartment of Electrical Engineering, National Taiwan University of Science and TechnologyDepartment of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical CenterDepartment of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of MedicineDepartment of Psychiatry, Far Eastern Memorial HospitalDepartment of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of MedicineDivision of Medical Image, Far Eastern Memorial HospitalAbstract Delays or misdiagnoses in detecting pneumoperitoneum can significantly increase mortality and morbidity. We developed and validated a deep learning model designed to identify pneumoperitoneum in computed tomography images. The model is trained on abdominal scans from Far Eastern Memorial Hospital (January 2012–December 2021) and evaluated using a simulated test set (14,039 scans) and a prospective test set (6351 scans) collected from the same center between December 2022 and May 2023. External validation included 480 scans from Cedars-Sinai Medical Center. Overall, the model achieves a sensitivity of 0.81–0.83 and a specificity of 0.97–0.99 across retrospective, prospective, and external validation; sensitivity improves to 0.92–0.98 when cases with a small amount of free air (total volume <10 ml) are excluded. These findings suggest that the model can deliver accurate and consistent predictions for pneumoperitoneum in computed tomography scans with segmented masks, potentially accelerating diagnostic and treatment workflows in emergency care.https://doi.org/10.1038/s41467-024-54043-1 |
| spellingShingle | I-Min Chiu Teng-Yi Huang David Ouyang Wei-Che Lin Yi-Ju Pan Chia-Yin Lu Kuei-Hong Kuo PACT-3D, a deep learning algorithm for pneumoperitoneum detection in abdominal CT scans Nature Communications |
| title | PACT-3D, a deep learning algorithm for pneumoperitoneum detection in abdominal CT scans |
| title_full | PACT-3D, a deep learning algorithm for pneumoperitoneum detection in abdominal CT scans |
| title_fullStr | PACT-3D, a deep learning algorithm for pneumoperitoneum detection in abdominal CT scans |
| title_full_unstemmed | PACT-3D, a deep learning algorithm for pneumoperitoneum detection in abdominal CT scans |
| title_short | PACT-3D, a deep learning algorithm for pneumoperitoneum detection in abdominal CT scans |
| title_sort | pact 3d a deep learning algorithm for pneumoperitoneum detection in abdominal ct scans |
| url | https://doi.org/10.1038/s41467-024-54043-1 |
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